Efficient Image-Based Methods for Rendering Soft Shadows. Hard vs. Soft Shadows. IBR good for soft shadows. Shadow maps

Transcription

1 Efficient Image-Based Methods for Rendering Soft Shadows Hard vs. Soft Shadows Maneesh Agrawala Ravi Ramamoorthi Alan Heirich Laurent Moll Pixar Animation Studios Stanford University Compaq Computer Corporation Compaq Computer Corporation SIGGRAPH 2001 Hard Shadows Soft Shadows Shadow maps Image-based hard shadows [Williams 78] Time, memory depend on image size, not geometric scene complexity Disadvantage: bias and aliasing artifacts Soft shadows [Chen and Williams 93] View interpolate multiple shadow maps IBR good for soft shadows IBR good for secondary effects Artifacts less perceptible IBR works well for nearby viewpoints Shadow maps from light source Light source localized area Poorly sampled regions are also dimly lit

2 IBR good for soft shadows Poorly sampled regions are also dimly lit Shadow map Light Contributions Extend shadow maps to soft shadows Image-based rendering especially suitable Two novel image-based algorithms: Layered attenuation maps (LAM) Coherence-based raytracing (CBRT) Attenuation only With lighting LAM Display: 5-10 fps Some aliasing artifacts Interactive applications Games Previewing Preliminaries CBRT Render: min Speedup: 12.96x Production quality images

3 Refresher: LDIs Layered depth images [Shade et al. 98] Camera Refresher: LDIs Layered depth images [Shade et al. 98] LDI Geometry Refresher: LDIs Layered depth images [Shade et al. 98] LDI (Depth, Color) Precomputation Render views from points on light (hardware) Create layered attenuation map (software) Warp views into LDI Store (depth, attenuation) Objects in LAM visible in at least 1 view

4 Precomputation Precomputation 1 st viewpoint 2 nd viewpoint Attenuation = 2/2 Attenuation = 1/2 Precomputation Display Attenuation = 2/2 Warped 2 nd viewpoint Render scene without shadows (hardware) Project into LAM (software) Read off attenuation Attenuation modulates shadowless rendering Not present Attenuation = 1/2

5 Display LAM (center of light) Display LAM (center of light) Eye Eye Attenuation = 2/2 Color = Color * 2/2 Display LAM (center of light) Display LAM (center of light) Eye Eye Not in LAM Attenuation = 0 Color = Color * 0

6 Precompute algorithm Illustration Rendered images from light Layered images

7 Layered attenuation map Display algorithm Attenuation map and rendering 1st layer LAM size: 512 x 512 Avg num depth layers: 1.5 Precomp: 7.7 sec (64 views) 29.4 sec (256 views) Display: 5-10 fps one layer 2nd layer attenuation map rendering

8 LAM size: 512 x 512 Avg num depth layers: 2 Precomp: 6.0 sec (64 views) 22.4 sec (256 views) Display: 5-10 fps LAM Layered attenuation maps fast, aliases Coherence-based raytracing slow, noise CBRT Coherence-based raytracing Hierarchical raytracing through depth images Time, memory decoupled from geometric scene complexity Coherence-based sampling Light source visibility changes slowly Reduce number shadow rays traced Also usable with geometric raytracer Image-based raytracing Light 1 st shadow map Represent scene with multiple shadow maps

9 Image-based raytracing Light 1 st shadow map 2 nd shadow map Image-based raytracing Light 1 st shadow map 2 nd shadow map Represent scene with multiple shadow maps Trace shadow ray through shadow maps Light source visibility image Light source visibility image Visibility image Light Vis image for s 1 Visibility image Light s 1 s 1 s 2

10 Coherence-based sampling Compute visibility image at first point s 1 Loop over following surface points s i Predict visibility image at s i from s i-1 Trace rays where prediction confidence low Predicting visibility Blocker pts s 1 s 1 s 2 Prediction Predicting visibility Prediction Prediction confidence Low confidence Light source edges Blocked/unblocked edges Blocker pts s 1 s 1 s 2 Trace rays in all X ed cells High confidence: 5 Low confidence: 31 Total cells: 36 Ratio: 5/36 = 0.14 Predicted visibility

11 Prediction confidence Low confidence Light source edges Blocked/unblocked edges Propagating low confidence If traced ray = prediction trace neighbor cells Trace rays in all X ed cells High confidence: 56 Low confidence: 88 Total cells: 144 Ratio: 56/144 = 0.40 Predicted visibility Similar to [Hart et al. 99] Prediction correct Propagating low confidence If traced ray = prediction trace neighbor cells Light cells: 16 x 16 (256) Four 1024 x 1024 maps Precomp: 2.33 min Render: min Rays: Speedup: 12.96x 2.27x due to image-based raytracing accelerations 5.71x due to coherence-based sampling Prediction incorrect

12 LAM Light cells: 16 x 16 (256) Four 1024 x 1024 maps Precomp: 3.93 min Render: min Rays: Speedup: 8.52x 2.16x due to image-based raytracing accelerations 3.94x due to coherence-based sampling Ray tracing CBRT

13 LAM CBRT Conclusions Two efficient image-based methods Layered attenuation maps Interactive applications Coherence-based raytracing Production quality images IBR ideal for soft shadows secondary effects